Abstract
This paper presents an interactive game designed to improve users' experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL's effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility.

Abstract
The presence of missing data poses a common challenge for time series analysis in general since the most usual requirement is that the data is equally spaced in time and therefore imputation methods are required. For time series of counts, the usual imputation methods which usually produce real valued observations, are not adequate. This work employs Bayesian principles for handling missing data within time series of counts, based on first-order integer-valued autoregressive (INAR) models, namely Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. The methodologies are illustrated with synthetic and real data and the results indicate that the estimates are consistent and present less bias when the percentage of missing observations decreases, as expected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
We present an adequacy theorem for a concurrent extension of probabilistic GCL. The underlying denotational semantics is based on the so-called mixed powerdomains, which combine non-determinism with probabilistic behaviour. The theorem itself is formulated via M. Smyth's idea of treating observable properties as open sets of a topological space. The proof hinges on a 'topological generalisation' of Konig's lemma in the setting of probabilistic programming (a result that is proved in the paper as well). One application of the theorem is that it entails semi-decidability w.r.t. whether a concurrent program satisfies an observable property (written in a certain form). This is related to M. Escardo's conjecture about semi-decidability w.r.t. may and must probabilistic testing.

Abstract
The increasing global burden of lung diseases necessitates the development of improved diagnostic tools. According to the WHO, hundreds of millions of individuals worldwide are currently affected by various forms of lung disease. The rapid advancement of artificial neural networks has revolutionized lung disease diagnosis, enabling the development of highly effective detection and classification systems. This article presents dual channel neural networks in image feature extraction based on classical CNN and vision transformers for multi-label lung disease diagnosis. Two separate subnetworks are employed to capture both global and local feature representations, thereby facilitating the extraction of more informative and discriminative image features. The global network analyzes all-organ regions, while the local network simultaneously focuses on multiple single-organ regions. We then apply a novel feature fusion operation, leveraging a multi-head attention mechanism to weight global features according to the significance of localized features. Through this multi-channel approach, the framework is designed to identify complicated and subtle features within images, which often go unnoticed by the human eye. Evaluation on the ChestX-ray14 benchmark dataset demonstrates that our hybrid model consistently outperforms established state-of-the-art architectures, including ResNet-50, DenseNet-121, and CheXNet, by achieving significantly higher AUC scores across multiple thoracic disease classification tasks. By incorporating test-time augmentation, the model achieved an average accuracy of 95.7% and a specificity of 99%. The experimental findings indicated that our model attained an average testing AUC of 87%. In addition, our method tackles a more practical clinical problem, and preliminary results suggest its feasibility and effectiveness. It could assist clinicians in making timely decisions about lung diseases.

Abstract
A Learning management system (LMS) is considered appropriate for company training. It is increasingly used in companies or organizations as a tool to manage their online training. The company or organization should consider the implementation of an LMS that provides ease in training content selection to achieve the best use and satisfaction of its employees in the learning process. From this perspective, the present study aims to investigate the implementation of a personalized LMS to facilitate the formative content selection tailored to employees’ roles. A Survey research methodology was used to achieve this objective. Based on the literature and survey results, we propose an approach to reach the personalization of content selection.

Abstract
Entity linking is an important task in medical natural language processing (NLP) for converting unstructured text into structured data for clinical analysis and semantic interoperability. However, in lower-resource languages, this task is challenging due to the limited availability of domain-specific resources. This paper explores a translation-based cross-lingual entity linking approach using GPT models, GPT-3.5 and GPT-4o, for zero-shot machine translation and entity linking with in-context learning. We evaluate our approach using a Portuguese-English parallel dataset of radiology abstracts. Our results show that chunk-level machine translation outperforms sentence-level translation. Moreover, our translationbased approach to cross-lingual entity linking of UMLS concepts outperformed the multilingual encoder method baseline. However, the in-context learning entity linking approach did not outperform a translation-based approach with a dictionary-based entity linking method.