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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions.

Abstract
Virtual reality (VR) offers an immersive experience by simulating realistic environments, reducing the need for physical spaces and equipment. This technology addresses the challenges of traditional testing and learning, which often involve high costs, hardware limitations, and health concerns. In this paper, we use Blender, an open-source 3D creation software, to model a virtual vineyard and a wine sensory analysis laboratory. The vineyard is populated with various plants across a large virtual space, and the sensory lab is designed to simulate wine-tasting processes. The virtual lab enables users to explore and understand the sensory characteristics of wine in a controlled, immersive environment, providing an effective alternative to physical labs. Our simulations demonstrate that VR can replicate real-world environments with high fidelity and improve user engagement. The results suggest that using VR for wine sensory analysis can enhance educational outcomes, reduce costs, and offer an innovative platform for research and learning.

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Abstract
We present a novel solution for automatic task allocation in multidevice environments, where configured robots compete for task assignment when announcing tasks, minimizing manual intervention. To this end, we propose the specification of a task assignment system and a task-oriented programming method aimed at automating processes and optimizing resource utilization in multiple controller environments. The proposed solution with its market-based algorithm and developed architecture improves the adaptability, scalability and overall efficiency of the system. The research discussion extends to broader implications that are consistent with the overall goal of improving robot capabilities in various deployment scenarios.