Abstract
In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles' autonomous driving and optimal route calculation.

Abstract
Problem: Portable ECG/sEMG acquisition systems for telemedicine often lack application flexibility (e.g., limited configurability, signal validation) and efficient wireless data handling. Methodology: A modular biosignal acquisition system with up to 8 channels, 24-bit resolution and configurable sampling (1-4 kHz) is proposed, featuring per-channel gain/source adjustments, internal MUX-based reference drive, and visual electrode integrity monitoring; Bluetooth (R) transmits data via a bit-wise packet structure (83.92% smaller than JSON, 7.28 times faster decoding with linear complexity based on input size). Results: maximum 6.7 mu V-rms input-referred noise; harmonic signal correlations >99.99%, worst-case THD of -53.03 dBc, and pulse wave correlation >99.68% in frequency-domain with maximum NMSE% of 6e-6%; and 22.3-hour operation (3.3 Ah battery @ 150 mA). Conclusion: The system enables high-fidelity, power-efficient acquisition with validated signal integrity and adaptable multi-channel acquisition, addressing gaps in portable biosensing.

Abstract
This article presents a systematic review of the most prevalent vulnerabilities plaguing web and mobile applications. By analyzing recent research, it identifies a core set of vulnerabilities, including injection flaws, broken authentication, cross-site scripting (XSS), and insecure direct object references. Recognizing the human element, the article acknowledges the role of social engineering in exploiting these technical weaknesses. The review delves deeper, exploring how these vulnerabilities manifest differently across web and mobile platforms, considering factors like server-side security and API access. The research concludes by advocating for a defense strategy, emphasizing the importance of secure coding practices, robust authentication, and user awareness training. This comprehensive approach paves the way for a more secure digital landscape where both web and mobile applications can thrive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The Industry 4.0 paradigm (I4.0) supports the improvement of industrial processes through Information and Communication Technologies (ICT), with information systems providing real-time information to humans and machines, in order to make the production process more flexible and efficient. In this context, Virtual Assistants (VA) collect and process production data and provide contextualized and real-time information to the workers in the production environment. This paper presents a prototype of a VA developed to collect production data from heterogeneous sources in the factory, process them based on contextual information, and provide workers with useful information to assist them in taking informed decisions. In that context, VA can represent a valuable aid to improve overall productivity and efficiency in the I4.0 factories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
Featured Application This review highlights interoperability, automation, and decision-making as critical requirements for context-aware systems in the manufacturing domain that integrate the principles of Industry 4.0. It discusses relevant patterns and technologies, identifies context gaps, emphasises ontologies' importance, and proposes directions for future research.Abstract Technological evolution has driven the integration of computing devices in various domains, giving rise to heterogeneous and dynamic intelligent environments; together with market pressure, these pose challenges in formulating an architecture that takes advantage of contextual knowledge. In terms of architectural design, we are witnessing a transition from a centralised, monolithic view of systems to a decentralised view that incorporates the vertical and horizontal dimensions of the production environment. Therefore, this review aimed to (i) identify the requirements, (ii) find out about the representation models and context inference techniques, and (iii) identify architectural technologies, norms, models, and standards. The results observed in 25 articles made it possible to identify interoperability, automation, and decision-making as convergence points and observe the adoption of ontologies as a research area for context representation. In contrast, the discussion of context inference techniques remains open. Finally, this study presents recommendations for the design of a context-aware systems architecture that incorporates the principles of Industry 4.0 and facilitates the development of applications.