Abstract
This study examines the relationship between quality of financial information (QFI) and the probability of corporate decline in the food manufacturing sector within the European Union (EU) controlled by a set of internal (age, size, liquidity, return on assets, and debt) and external (gross domestic product and unemployment rate) determinants. The study employs a logit regression model applied to a balanced panel dataset of 335 large food manufacturing firms in the EU from 2011 to 2021. Quality of financial information is estimated using discretionary accruals, based on the Jones model (1991), while corporate decline is measured by fluctuations in sales. The findings indicate that low-quality financial information (high discretionary accruals) is positively associated with corporate decline, highlighting the role of financial transparency in business sustainability. The results suggest that earnings management practices can increase business vulnerability, reinforcing the importance of accurate financial reporting in mitigating corporate failure. The study underscores the need for enhanced regulatory oversight and financial reporting transparency in the food manufacturing industry. Policymakers and stakeholders should strengthen financial disclosure requirements to curb earnings management practices and ensure better resource allocation for long-term sustainability. This research contributes to the limited literature on quality of financial information and corporate decline, particularly in the food manufacturing sector, which is crucial for economic stability and public welfare. By integrating financial reporting quality into corporate failure analysis, this study provides new insights into the role of earnings management in business deterioration.

Abstract
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. Most existing approaches assume the missing labels as negatives and/or exploit image and category relationships to regularize training. Orthogonally, this paper studies blending samples in such incomplete datasets as new samples, extending the training data magnitude to increase generalization. First, the proposed LogicMix mixes multiple partially labeled samples to produce new samples, where their unknown labels are naturally mixed by OR's logical equivalences, without replacement with constants. Subsequently, a Decouple Partial-Asymmetric Loss is proposed to assign separate label-focusing policies to original and new samples, addressing the learning imbalance from the different positive-negative label imbalances between original and augmented samples. Finally, we propose a complete learning framework called 2WayAug-PL. LogicMix and conventional data augmentation collaborate to extend the diversity of new samples in both the sample-sample relation and human prior knowledge, while pseudo-labeling compensates for the lack of labels to provide more supervision signals. 27 partially labeled dataset scenarios derived from three benchmarking datasets with various learning difficulties are utilized for comprehensive experiments. LogicMix has shown remarkable effectiveness and generality in improving mAP against compared sample-mixing data augmentation methods. In particular, 2WayAug-PL achieves state-of-the-art average mAP of 84.3%, 50.1 %, and 93.8% on MS-COCO, VG-200, and Pascal VOC 2007, respectively. It further pushes the previous best performance achieved by different frameworks by 0.6% (CFT), 0.6% (CFT), and 0.1 % (SR). Moreover, 2WayAug-PL significantly outperforms all compared frameworks, as shown by statistical tests. Code is available at: https://github.com/maxium0526/logic_mix.

Abstract
This study examines how the quality of financial information (QFI), using financial reporting discretion, influences trade credit decisions for Portuguese medium-sized retail companies, particularly in contexts of bank financing constraints (2012 to 2022). Findings indicate that isuppliers respond more to optimistic financial signals-specifically, income-increasing discretionary accruals-than to intrinsic accounting quality. Instead of relying purely on formal data, suppliers prioritize relational and operational factors, such as trust, interpersonal relationships, debt history, payment terms and operational profitability. Additionally, company size, asset tangibility and profitability negatively influence trade credit. Firms with low asset tangibility tend to utilize trade credit more frequently because they lack sufficient collateral to secure bank financing. As tangibility increases, these firms may access bank credit at lower costs and consequently substitute trade credit. A similar pattern is observed for firm size, with smaller firms relying more heavily on supplier credit, whereas larger firms display lower dependence on this financing channel. Moreover, greater availability of internal resources reduces the need for supplier-based financing. Our results contradict part of the existing literature, which predicts positive relationships between trade credit, firm size, tangibility and profitability. The study highlights the importance of fostering trust between companies and suppliers, as well as enhancing financial transparency to secure more favorable financing conditions.

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
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.