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. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract

Abstract
This paper presents ElderMind, a mobile application designed to promote cognitive stimulation and engagement among older adults. Developed using a User-Centered Design (UCD) approach, the application incorporates gamified elements to enhance usability. ElderMind features three cognitive games-memory, puzzle, and maze-solving-each with adjustable difficulty levels, ensuring accessibility for diverse user needs. Key functionalities include performance tracking, customizable font sizes, and multilingual support, making it a versatile tool for aging populations. Accessibility and usability assessments were conducted to refine the application iteratively, addressing issues such as visual contrast and touch target sizes. Preliminary usability testing with participants aged 50-64 demonstrated ease of use, with most tasks rated as not difficult at all. Feedback highlighted the application's simplicity and accessibility while identifying areas for improvement, such as interface aesthetics and game variety. ElderMind represents a preliminary solution toward inclusive digital solutions for cognitive health and user engagement.

Abstract
The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.

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.